The BOREAS TE-06 team collected several data sets in support of its efforts to characterize and interpret information on the plant biomass, allometry, biometry, sapwood, leaf area index, net primatry production, soil temperature, leaf water potential, soil CO2 flux, and multivegetation imagery of boreal vegetation. This data set includes tree measurements conducted on the above gound biomass of trees in the BOREAS NSA and SSA during the growing seasons of 1994 and 1995 and the derived allometric relationships/equations.

A global data set of root biomass, rooting profiles, and concentrations nutrients in roots was compiled from the primary literature and used to study distributions of root properties. This data set consists of estimates of fine root biomass and specific area, site characteristics, and source references associated with two papers (Jackson et al. 1996 and 1997).Understanding and predicting ecosystem functioning (e.g., carbon and water fluxes) and the role of soils in carbon storage requires an accurate assessment of plant rooting distributions.

The BOREAS TE-06 team collected several data sets in support of its efforts to characterize and interpret information on the plant biomass, allometry, biometry, sapwood, leaf area index, net primary production, soil temperature, leaf water potential, soil CO2 flux, and multivegetation imagery of boreal vegetation. This data set contains measurements of estimates of the standing biomass and leaf area index for the plant species at the TF, CEV, and AUX sites in the SSA and NSA during the growing seasons of 1994 and 1995.

The BOREAS TE-22 team collected data sets in support of its efforts to characterize and interpret information on the forest structure of boreal vegetation in the SSA and NSA during the 1994 growing season.

This dataset provides gridded estimates of aboveground biomass (AGB) for live dry woody vegetation density in the form of both stock for the baseline year 2003 and annual change in stock from 2003 to 2016. Data are at a spatial resolution of approximately 500 m (463.31 m; 21.47 ha) for three geographies: the biogeographical limit of the Amazon Basin, the country of Mexico, and a Pantropical belt from 40 degrees North to 30 degrees South latitudes. Estimates were derived from a multi-step modeling approach that combined field measurements with co-located LiDAR data from NASA ICESat Geoscience Laser Altimeter System (GLAS) to calibrate a machine-learning (ML) algorithm that generated spatially explicit annual estimates of AGB density. ML inputs included a suite of satellite and ancillary spatial predictor variables compiled as wall-to-wall raster mosaics, including MODIS products, WorldClim climate variables reflecting current (1960-1990) climatic conditions, and SoilGrids soil variables. The 14-year time series was analyzed at the grid cell (~500 m) level with a change point-fitting algorithm to quantify annual losses and gains in AGB. Estimates of AGB and change can be used to derive total losses, gains, and the net change in aboveground carbon density over the study period as well as annual estimates of carbon stock.

The Biophysical Properties of the Vegetation Data Set were collected as part of the larger FIFE Science effort to characterize the physical and biological properties of the sites within the FIFE study area over the life of the field experiment. These data were collected at 43 locations scattered throughout the FIFE study area between May 1987 and August 1989. The measurements of leaf area were based on an optical technique in which the area of the light beam obscured by the material under the beam is a measure of the surface area of that material relative to the total surface area that the beam covers. The resulting Leaf Area Indices (LAI) provide a relative measure of leaf area. These indices, when compared between plant samples provide an indirect and relative measure of plant biomass.

This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31.

This dataset provides plot-level estimates of basal area, aboveground biomass, number of trees, maximum tree height, and basal-area-weighted wood specific gravity that were derived from observations of nearly 6,700 individual trees including tree family, species, DBH, the height of each tree, and their x, y location within 25 x 25 m subplots. These field data were collected from 15 1-hectare plots located across the Mondah Forest of Gabon as part of the AfriSAR Campaign in 2016. These biophysical and biomass data were used for training models to derive the AfriSAR remote sensing-based aboveground biomass products.

This dataset includes estimates of annual forest aboveground biomass over the state of Maryland, USA, for the period 1984-2023. It was generated by a modeling approach that linked an ecosystem model called Ecosystem Demography (ED) model, airborne lidar data of canopy height in circa 2010, and the remote sensing based land cover change dataset (NAFD).